One way to improve peak picking would be to train an mProphet peak scoring model, and that will cause Skyline to look at more features when picking peaks, and would give you a false discovery rate.
Here is the tutorial for advanced peak picking models:
The problem with doing this with PRM data is that the mass spectrometer really needs to collect data for decoy peptides. There is no way to train a model if all of the data was collected for peptides that exist-- the mass spectrometer needs to have also collected data for some other random precursors.
You could collect a few more runs' worth of data with targets and decoys, and then train a peak picking model, and then use that model on the data that you already have.
Other than that, I do not know of a way to make your life easier, but someone else on this support board might have some good ideas.
If you would like, you could send us your dataset. We are always looking for examples of cases where Skyline does a less than perfect job as we look for ways to improve our peak picking in the future.
In Skyline you can use the menu item:
File > Share
to create a .zip file containing your Skyline document and supporting files including extracted chromatograms.
If that .zip file is less than 50MB you can attach it to this support request.
Otherwise, you can upload it here: