Computer models of cardiac electrophysiology (EP) can be a very efficient tool to better understand the mechanisms of arrhythmias. Quantitative adjustment of such models to experimental data (personalization) is needed in order to test their realism and predictive power, but it remains challenging at the organ scale. In this paper, we propose a framework for the personalization of a 3-D cardiac EP model, the Mitchell-Schaeffer (MS) model, and evaluate its volumetric predictive power under various pacing scenarios. The personalization was performed on ex vivo large porcine healthy hearts using diffusion tensor MRI (DT-MRI) and optical mapping data. The MS model was simulated on a 3-D mesh incorporating local fiber orientations, built from DT-MRI. The 3-D model parameters were optimized using features such as 2-D epicardial depolarization and repolarization maps, extracted from the optical mapping. We also evaluated the sensitivity of our personalization framework to different pacing locations and showed results on its robustness. Further, we evaluated volumetric model predictions for various epi- and endocardial pacing scenarios. We demonstrated promising results with a mean personalization error around 5 ms and a mean prediction error around 10 ms (5% of the total depolarization time). Finally, we discussed the potential translation of such work to clinical data and pathological hearts.