The Santa Fe Ant model problem has been extensively used to investigate, test and evaluate Evolutionary Computing systems and methods over the past two decades. There is however no literature on its program structures that are systematically used for fitness improvement, the geometries of those structures and their dynamics during optimization. This paper analyzes the Santa Fe Ant Problem using a new phenotypic schema and landscape analysis based on executed instruction sequences. For the first time we detail systematic structural features that give high fitness and the evolutionary dynamics of such structures. The new schema avoids variances due to introns. We develop a phenotypic variation method that tests the new understanding of the landscape. We also develop a modified function set that tests newly identified synchronization constraints. We obtain favorable computational efforts compared to those in the literature, on testing the new variation and function set on both the Santa Fe Trail, and the more computationally demanding Los Altos Trail. Our findings suggest that for the Santa Fe Ant problem, a perspective of program assembly from repetition of highly fit responses to trail conditions leads to better analysis and performance.