High-throughput drug sensitivity screening has been utilized for facilitating the discovery of drug combinations in cancer. Many existing studies adopted a dose-response matrix design, aiming for the characterization of drug combination sensitivity and synergy. However, there is lack of consensus on the definition of sensitivity and synergy, leading to the use of different mathematical models that do not necessarily agree with each other. We proposed a cross design to enable a more cost-effective testing of sensitivity and synergy for a drug pair. We developed a drug combination sensitivity score (CSS) to summarize the drug combination dose-response curves. Using a high-throughput drug combination dataset, we showed that the CSS is highly reproducible among the replicates. With machine learning approaches such as Elastic Net, Random Forests and Support Vector Machines, the CSS can also be predicted with high accuracy. Furthermore, we defined a synergy score based on the difference between the drug combination and the single drug dose-response curves. We showed that the CSS-based synergy score is able to detect true synergistic and antagonistic drug combinations. The cross drug combination design coupled with the CSS scoring facilitated the evaluation of drug combination sensitivity and synergy using the same scale, with minimal experimental material that is required. Our approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput drug combination screening. The R scripts for calculating and predicting CSS are available at https://github.com/amalyutina/CSS