Density functional theory (DFT) calculations and machine learning (ML) methods are used to establish a relationship between the crystal structures of rare-earth (RE) disilicates (RE2Si2O7) and their coefficient of thermal expansion (CTE). The DFT total energy data predict the presence of several energetically competing crystal structures, which is rationalized as one of the reasons for observing polymorphism. An ensemble of support vector regression models is trained to rapidly predict the CTE as a function of RE2Si2O7 crystal chemistry. Experiments subsequently validated the structure and CTE predictions for Sm2Si2O7.