I've hit & missed by tweaking targeting of my custom audiences, but for criteria like OS (where it's mandatory to split in adgroups) or sometimes others such as gender (not mandatory by the platform, but might be more effective if creatives are very differenciated or the product catters specifically for one), I recommend having them separated in the seed audiences, for the reason explained above
Taking a specific example, imagine a country of 100m users with 50/50 Android/iOS:
- a custom audience of mixed OS would generate a lookalike 2% of 2 million people, which once filtered will be cut by roughly half (not exactly, illustration purpose only) = 1m targeted users from a LAL2%
- a custom audience of 1 specific audience would generate a lookalike 1% of 1 million people, once filtered will be almost identitical = 1m targeted users from a LAL1%
The LAL2% is likely to be "broader" than the LAL1% hence less effective.
Seeing this tactic working, I started building multiple seeds, adding things like age, languages etc but this reached a limit and at some point it wasn't worth the effort (added complexity, audience management...).
Also: this applies well for very large geos (or tiers), less so when creating audiences for smaller geos.