Well, then why spend all this time searching in a lab? Instead, run a bunch of simulations until the compounds with properties you desire just sort of pop out as the top hits in your simulations?
(I say this as somebody with a decade+ of experience running large ensembles of classical MD simulations, but not so much experience with inorganic DFT)
There are high throughput DFT projects (e.g., the materials project) but (1) the calculations are very computationally expensive and (2) the search space is really large. People are doing cool stuff with ML and generative models, but it’s a pretty open research area still
Also, at the end of the day, DFT is still an imperfect approximate model. Relative trends are generally more reliable than exact correspondence with experiment, and it can have system-specific systematic errors that are hard to account for in a high throughput setting
Crystal structure prediction by itself is a very hard problem. Just given the chemical formula and asking "what is the most stable structure" is a global optimization problem. And this is just for one single composition. It isn't an easy global optimization problem either. You not only have to determine the unit cell vectors, the positions of the atoms in the unit cell, but also the number of atoms in the unit cell needed to represent the structure! Just because you are even given "Pb9Cu(PO4)6O" as the formula finding the most stable structure, let alone the superconducting non-minimum energy state is a huge undertaking. Now expand that to all of chemical space and you can see that it is not that easy!
Edit: Also look at how long these (short pre-print) DFT articles are. These aren't simple calculations to interpret.
If you run a bunch of simulations to brute-force-find what you're looking for, you run into a problem of infinitely many things to simulate. It is better to use domain knowledge to eliminate things that don't work and narrow down things that would have high chance of working in theory to give some sort of directional guidance for your research.
That domain knowledge wouldn't have suggested exploring this space for superconductivity.
Turn the material science problem around: instead of looking for a substance that has a specific property, look at many substances until small amounts of any interesting property (young's modulus, etc) show up. By looking for "anything interesting" you are more likely to find something of interesting (ideally, several somethings). And then you also know a starting place to begin optimiziation.
(I'm not saying these things out of ignorance; this technique has worked well for me at times when I had exceptionally large amounts of CPU available to me, and it's also worked well in the drug industry, which has similar problems to material science.)
I think the first insight to pursue LK-99 by the researchers was from the deceased scholar from their graduate school (department chair I believe?). The material was already found in 1999, but they need to try different synthesis methods over 1,000 times for slightly different chemical compositions. I am not sure if there are simulation methods to do that, but it was definitely theoretical insight that first convinced their teacher to start, and the work made the pupils to believe in what they are pursing as far as apparent background stories are concerned.
(I say this as somebody with a decade+ of experience running large ensembles of classical MD simulations, but not so much experience with inorganic DFT)