In conclusion, we have demonstrated that the SISSO algorithm can generate ML models to predict the excited-state properties of molecular crystals using only a small amount of GW +BSE training data by incorporating physical/chemical knowledge into the selection of primary features. The resulting ML models, which require only relatively low-cost DFT calculations to evaluate, can provide estimates for excited-state properties of molecular crystals including the optical gap, triplet exciton energy, singlet-triplet gap, and exciton binding energy. These properties would not otherwise be accessible via DFT calculations. Using ML models in the early stages of materials screening workflows can effectively narrow down the number of candidates selected for further evaluation using computationally expensive excited-state theory. ML models can thus significantly accelerate the discovery of crystalline organic semiconductors with desirable properties for applications in optoelectronic devices.